监督机器学习模型在太阳能光伏系统中用于电气故障检测的有效性
Ved Khandeparkar1, Shreshtha1, Senthil Kumar Ramu2
1School of Electrical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, 600127, India.
Scientific reports
|October 7, 2025
概括
机器学习算法有效地检测和分类光伏 (PV) 系统的故障,包括短路和开放电路. 这项研究通过先进的故障检测方法提高了光伏系统的可靠性.
科学领域:
- 电气工程 电气工程
- 可再生能源系统可再生能源系统
- 人工智能的人工智能
背景情况:
- 光伏 (PV) 系统对于可再生能源至关重要,但面临着整合挑战.
- 故障会对光伏电站的生产和运行寿命产生重大影响.
- 可靠的故障检测对于电网稳定性和光伏系统效率至关重要.
研究的目的:
- 开发和评估用于检测和分类各种光伏系统故障的机器学习 (ML) 算法.
- 评估决策树 (DT),天真湾 (NB),随机森林 (RF),支持矢量机 (SVM) 和XGBoost算法的性能.
- 用MATLAB/Simulink分析故障对光伏系统参数的影响.
主要方法:
- 使用了ML算法 (DT,NB,RF,SVM,XGBoost) 来进行故障分类.
- 在MATLAB/Simulink中进行模拟,以模拟故障场景.
- 在故障条件下分析电压,电流和功率变化.
主要成果:
- 取得了很高的分类准确度:XGBoost (98.0%),NB (97.60%),SVM (97.40%),DT (97.20%),RF (97.20%). 获得了很高的分类准确度:XGBoost (98.0%),NB (97.60%),SVM (97.40%),DT (97.20%),RF (97.20%). 获得了很高的分类准确度:XGBoost (98.0%),NB (97.60%),SVM (97.40%),DT (97.20%),RF (97.20%).
- 使用混矩阵和相关热图验证了分类有效性.
- 证明了ML在识别短路 (SC),开放电路 (OC),地面故障 (GF) 和不匹配故障 (MF) 的能力.
结论:
- ML算法为光伏电气故障检测和分类提供了强大的解决方案.
- 智能监控,基于物联网的检测和预测分析对于提高光伏系统可靠性至关重要.
- 该研究强调了先进的ML技术对于安全高效的光伏能源整合的重要性.
相关概念视频
Three-Phase Short Circuit—Unloaded Synchronous Machine
667
Conducting a three-phase short circuit test on an unloaded synchronous machine helps understand its impact on the system. The AC fault current's oscillogram, with the DC offset removed, reveals that the waveform amplitude decreases from an initially high value to a steady-state level for one phase of the machine.
This behavior occurs due to the magnetic flux produced by the short-circuit armature currents. Initially, these currents follow high-reluctance paths but eventually shift to...
This behavior occurs due to the magnetic flux produced by the short-circuit armature currents. Initially, these currents follow high-reluctance paths but eventually shift to...
667
Power System Three-Phase Short Circuits
523
Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
523
Simplified Synchronous Machine Model
747
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
In this model, each generator is connected to a...
747
Wind Turbine Machine Models
562
In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
562
The Power Flow Problem and Solution
836
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk, phase angle δk, real power Pk, and reactive power Qk. Two of these four variables are inputs, while the power flow program computes...
836
Survival Tree
385
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
385


